Intelligent ECG Monitoring Tailored for Women’s Health

A unified platform that organizes complex FDA datasets, drugs, devices, approvals, and recalls—into fast, accurate, and easy-to-understand insights.

Delivering continuous, AI-powered ECG insights designed specifically for women’s health.

A smarter way to monitor women’s health

Petal.today is an AI-powered health monitoring platform designed to address long-standing gaps in women’s healthcare. We built a continuous, discreet, and clinically intelligent system that helps women better understand their heart health and physiological patterns—without disrupting daily life.

The Core Intent of Our Approach

Our approach focused on creating a women-first monitoring experience that prioritizes comfort, data accuracy, clinical relevance, and long-term scalability.

Unify
health signals

Bring ECG, motion, breathing, and physiological signals together into a single, continuous monitoring experience tailored to women’s real-world health needs.

Transform
signal intelligence

Replace noisy, unreliable wearable data with AI-driven signal processing that removes motion artifacts and delivers clinically meaningful ECG and health insights.

Preserve
data trust

Maintain high data quality through automated quality checks, segmentation, and validation—ensuring insights are reliable for both users and clinicians.

Build
scalable architecture

Enable large-scale women’s health monitoring with a secure, cloud-native platform designed to support population growth, advanced analytics, and future clinical integrations.

Where women’s health monitoring breaks down

Women’s health data is often incomplete, episodic, and poorly
contextualized—making it difficult to detect early risks, understand long-term patterns, and deliver timely care in real-world settings.

Intermittent monitoring

Traditional checkups and short-term wearables capture only brief snapshots, missing subtle but critical changes in women’s heart health and physiology over time.

Motion-distorted signals

Daily movement introduces noise into wearable ECG data, leading to inaccurate readings, false alerts, and reduced confidence in health insights.

Limited data trust

Without automated quality checks, poor or corrupted signals can influence analysis, making it hard for clinicians and users to rely on the data.

Lack of women-centric design

Most monitoring solutions are built on male-centric research models, failing to reflect women’s unique biology, comfort needs, and long-term monitoring requirements.

How we turned raw signals into clinical intelligence

A structured, end-to-end workflow that transforms noisy, real-world wearable data into trusted, actionable insights, designed specifically for continuous women’s health monitoring.

Capture

real signals

Continuously collect ECG, motion, bioimpedance, and temperature data during daily activity, rest, and recovery.

Secure and contextual data

Encrypt and transmit data to the cloud, using motion context to separate stationary and active segments.

Clean and

validate signals

Remove motion noise, assess signal quality, block corrupted data, and extract ECG, HRV, and breathing metrics.

Deliver

actionable data

Present validated trends and alerts through clear dashboards for proactive health decisions.

From noisy signals to trusted health intelligence

This pipeline demonstrates how Petal.today transforms real-world wearable data,often affected by motion, noise, and signal loss—into clean, validated, and clinically meaningful health insights.

Raw signal

intake

ECG data is captured in real-world conditions, including noise, flatlines, and baseline drift.

Signal denoising

Advanced filtering removes motion artifacts and reconstructs clean ECG waveforms.

R-peak detection

Precise R-peaks are identified to enable accurate heart rate and HRV analysis.

Quality validation

Low-quality or corrupted segments are automatically detected and excluded.

Motion
fusion

Accelerometer data is combined with ECG to add real-time movement awareness.

Activity segmentation

Time segments are classified as stationary or non-stationary to isolate motion effects.

Breathing & HRV

Validated ECG data is used to derive breathing rate and heart variability metrics.

Fall
detection

Impact patterns and post-event inactivity are analyzed to detect falls and trigger alerts.

The Impact we delivered

Petal.today transformed noisy wearable data into trusted ECG
insights, enabling proactive, women-focused health monitoring and reliable clinical decision-making.

Improved confidence

|

Clinical clarity

|

Scalable platform

|

Stronger trust

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